Copyright © 2024 by Author/s and Licensed by Kuey. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited Educational Administration: Theory and Practice 2024, 30(6), 4127-4134 ISSN: 2148-2403 https://kuey.net/ Research Article Recent Trends In Supply Chain Management Using Artificial Intelligence And Machine Learning In Manufacturing Joseph Muthu 1* , Dilip Kumar Vaka 2 1* PhD Student, Email: josephmuthu45@gmail.com 2 Senior Manager, Email: dilipkumarvaka15@yahoo.com Citation: Joseph Muthu, (2024) Recent Trends In Supply Chain Management Using Artificial Intelligence And Machine Learning In Manufacturing, Educational Administration: Theory and Practice, 30(6), 4127-4134 Doi: 10.53555/kuey.v30i6.6499 ARTICLE INFO ABSTRACT AI solutions are meant to compute real-world applicable business solutions using multiple branches of business applications like machine learning: translates practical business systems, neural network industry-leading development applications (manufacturing) with user interface extended and intelligent to cut and give unique features based on process-specific matrix data, deep learning: transform computer field coping up to cognitive transformations and can accomplish multi-task automation with constant self-evolution learning solutions. Deep learning revolutionizing game- changer for: A) reinforced the learning fault-detection fault diagnostics (DNN), transfer learning for complex recommender systems (DNN), and conversation contextualizing (DNN Hokey's algorithm) on different areas of the manufacturing industry. B) Offers solutions for novel reinforcement algorithms like these Q-learning algorithms when traditional business AI procedures were time-consuming or non-stopping. While machine learning increases the changing business landscape, adopting AI in the manufacturing sector offers substantial long-term revenue savings, increasing the gap between competitive industries in the current competitive manufacturing world. AI provides new techniques for manufacturing industrial data analysis. This data has the potential to specialize in industrial manufacturing critical areas of application demand repair, maintenance forecasting causal reasoning, and improvement of decision-making. Manufacturing companies that invest in AI solutions in established profit lines demand an in-depth understanding of technically complex, pronounced business understandings, including overcoming adaptive metrics, and will ultimately be able to respond in real-time to any circumstances in their environment. Practical research proves that demand-related decisions related to AI can provide clear competitive advantages in established manufacturing business processes based on clear strategic business advantages gained from taking action by using AI and acquiring data. Keywords: Recent Trends in Supply Chain Management, Industry 4.0, Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Smart Manufacturing (SM), Computer Science, Data Science, Vehicle, Vehicle Reliability. 1. Introduction With the ever-growing number of IoT devices, different standards, frameworks, and models have been conceived to ensure security. However, minimal research is being undertaken to implement an adaptive security mechanism for the new race of IoTs that are powered by AI/ML. This paper proposes an adaptive security model for securing these IoT devices utilizing AI/ML risk assessment mechanisms through a constructed testbed. This framework allows users to utilize a hybrid risk assessment approach that primarily